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--- |
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base_model: |
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- openai/whisper-large-v3 |
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language: |
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- en |
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- zh |
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- de |
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- es |
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- ru |
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- ko |
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- fr |
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- ja |
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- pt |
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- tr |
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- pl |
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- ca |
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- nl |
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- ar |
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- sv |
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- it |
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- id |
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- hi |
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- fi |
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- vi |
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- he |
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- uk |
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- el |
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- ms |
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- cs |
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- ro |
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- da |
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- hu |
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- ta |
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- 'no' |
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- th |
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- ur |
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- hr |
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- bg |
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- lt |
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- la |
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- mi |
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- ml |
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- cy |
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- sk |
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- te |
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- fa |
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- lv |
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- bn |
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- sr |
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- az |
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- sl |
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- kn |
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- et |
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- mk |
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- br |
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- eu |
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- is |
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- hy |
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- ne |
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- mn |
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- bs |
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- kk |
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- sq |
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- sw |
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- gl |
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- mr |
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- pa |
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- si |
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- km |
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- sn |
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- yo |
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- so |
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- af |
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- oc |
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- ka |
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- be |
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- tg |
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- sd |
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- gu |
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- am |
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- yi |
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- lo |
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- uz |
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- fo |
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- ht |
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- ps |
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- tk |
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- nn |
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- mt |
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- sa |
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- lb |
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- my |
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- bo |
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- tl |
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- mg |
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- as |
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- tt |
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- haw |
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- ln |
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- ha |
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- ba |
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- jw |
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- su |
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library_name: transformers |
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license: apache-2.0 |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- asr |
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- Pytorch |
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- pruned |
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- audio |
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- automatic-speech-recognition |
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--- |
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# Whisper-large-v3-no-numbers |
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## Model info |
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This is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) model without number tokens (token ids corresponding to numbers are excluded). |
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NO fine-tuning was used. |
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Phrases with spoken numbers will be transcribed with numbers as words. It can be useful for TTS data preparation. |
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**Example**: Instead of **"25"** this model will transcribe phrase as **"twenty five"**. |
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## Usage |
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`transformers` version `4.45.2` |
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Model can be used as an original whisper: |
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```python |
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>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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>>> import torchaudio |
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>>> # load audio |
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>>> wav, sr = torchaudio.load("audio.wav") |
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>>> # resample if necessary |
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>>> wav = torchaudio.functional.resample(wav, sr, 16000) |
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>>> # load model and processor |
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>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-large-v3-no-numbers") |
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>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-large-v3-no-numbers") |
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>>> input_features = processor(wav[0], sampling_rate=16000, return_tensors="pt").input_features |
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>>> # generate token ids |
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>>> predicted_ids = model.generate(input_features) |
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>>> # decode token ids to text |
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>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) |
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['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Twenty seven years. <|endoftext|>'] |
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``` |
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The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. |
